Image Matching for Intelligent Monitoring Scene Adaptation
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Solution Overview
Problem
Intelligent monitoring systems require manual configuration and intervention to adapt to changes in monitoring scenes and lack automated functionality for maintaining normal operations, especially when monitoring devices are interfered with or blocked.
Innovation Solution
A method and apparatus for image matching that extracts feature points from a current plane layout image and matches them with reference images of standard samples, determining similarity through distance sets to automatically apply preset functions and trigger alarms when mismatches occur, thereby enhancing intelligence in image matching without manual configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual configuration is used to adapt monitoring systems to different scenes, then system adaptability is improved, but operation complexity and time consumption increase
Solution Approach 1:
The system pre-extracts feature points from standard monitoring scenes and stores them as reference data before actual matching is needed. This preliminary preparation enables rapid comparison and automatic identification when new scenes need to be adapted, eliminating manual configuration time while maintaining scene adaptability.
Solution Approach 2:
The patent creates feature point copies from standard monitoring scenes that can be rapidly compared against new scenes. These feature point representations serve as reusable templates that enable automatic scene identification and adaptation without requiring manual reconfiguration for each new monitoring environment.
2Measurement precision
If manual configuration is required for function settings, then system control precision is improved, but automation level deteriorates
Solution Approach 1:
The system performs self-service by automatically extracting feature points from monitoring scenes, comparing them against reference data, identifying matching scenes, and implementing corresponding functions without human intervention. This automation maintains high matching accuracy through algorithmic feature point comparison while eliminating the need for manual configuration.
Solution Approach 2:
The patent replaces manual mechanical configuration operations with automated image processing and feature point matching algorithms. The system uses computer vision techniques to automatically identify and match monitoring scenes, substituting human operators with intelligent algorithms that maintain precision while achieving full automation.
3Measurement precision
If feature point matching is performed across multiple reference images, then matching accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the reference images into distinct feature point sets, each representing specific monitoring scene characteristics. By dividing the reference data into manageable segments organized by scene type, the system can efficiently compare feature points without processing all reference images simultaneously, reducing computational complexity while maintaining matching precision.
Data Source
AI summary
Provided are a matching method and apparatus, an electronic device, and a computer-readable storage medium. The method includes: obtaining a to-be-matched image; extracting at least one to-be-matched feature point from the to-be-matched image; for one of at least one reference image, performing matching between the at least one to-be-matched feature point and at least one reference feature point extracted from the reference image to obtain a distance set corresponding to the reference image, the distance set corresponding to the reference image being a distance set composed of distances between the at least one to-be-matched feature point and the at least one reference feature point, the one reference image including an image of a standard sample, and different reference images including different standard samples; and determining, based on the distance set corresponding to each reference image, whether the to-be-matched image matches each reference image.